INTRODUCTION TO R AND DATA MANAGEMENT

Biostatistics Support and Research Unit

Germans Trias i Pujol Research Institute and Hospital (IGTP)
Badalona, Spain

March 19, 2025

Summary

1. Introduction to R

2. R objects

3. Numerical and logical operations

4. The tidyverse

5. Reading data

6. Data management

7. Joining data

8. Pivoting data

Introduction to R

What is R?

Free software language and environment for statistical computing and data management, visualization and analysis. It has become the language of the statisticians.

  • It provides a wide variety of statistical (descriptive statistics, classical statistical tests, linear and nonlinear modelling, time-series analysis, classification, clustering, …) and graphical techniques.

  • R is an open-source software so anyone can use, modify and write their own code and contribute to the community.

  • There are many resources for and by R users: rweekly.org, r-bloggers.com, … and lots of documentation online!

RStudio

  • RStudio is the most popular integrated development environment for R.

Rstudio


  • Source: to write your R code to a script file and save it. We can also run code in a script that is displayed in the console.

  • Console: to write and run R code directly.

  • Environments: to see the objects you create.

  • Output: to see the output of the executed code. The help is also displayed here.

R Objects

R objects

Pieces of information that are stored in R’s environment and can be viewed, referenced, or manipulated in some way.

  • Examples of values that can be stored in an object include:

    • A single/multiple numbers
    • Text
    • A dataset made up of rows of cases and columns of variables
    • The output from a fitted linear regression model
    • And much more!

Assign a value to an object

  • The assignment operator <- is used to assign a value to an object in R
#Assign the value 5 to the object x
x <- 5
#Show the value assigned to x
x
[1] 5

Tip

We can use the # symbol to add a comment in an R script.

Single value object

A single value that can be numeric, character, logical, datetime, etc.

→ Numeric

#Assign a numeric value
x <- 5
#Show value
x
[1] 5
#Show object class
class(x)
[1] "numeric"
#Check if object class is numeric
is.numeric(x)
[1] TRUE

Single value object

→ Character

x <- "bananas" 
x 
[1] "bananas"
class(x)
[1] "character"
is.character(x)
[1] TRUE

Single value object

→ Logical (TRUE/FALSE)

x <- TRUE 
x
[1] TRUE
class(x)
[1] "logical"
is.logical(x)
[1] TRUE

Single value object

→ Date

x <- Sys.Date()
x
[1] "2025-03-19"
class(x)
[1] "Date"

Vector

An ordered collection of the same type of single value objects.

  • We will usually use the function c() to create them:
#Numeric vector
x <- c(1, 3, 5)
x
[1] 1 3 5
class(x)
[1] "numeric"
  • To see the length of a vector we can use the function length():
length(x)
[1] 3

Vector

  • We can have different types of vectors depending on the type of single value objects they are composed of:
#Character vector
c("A", "B", "C")
[1] "A" "B" "C"
#Logical vector
c(TRUE, FALSE, FALSE)
[1]  TRUE FALSE FALSE
#Date vector
c(Sys.Date(), Sys.Date() - 1, Sys.Date() + 1)
[1] "2025-03-19" "2025-03-18" "2025-03-20"

Vector

  • We can create numeric vectors in alternative ways:
c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
 [1]  1  2  3  4  5  6  7  8  9 10

→ Using :

1:10
 [1]  1  2  3  4  5  6  7  8  9 10

→ Using the function seq()

seq(1, 10, by = 1)
 [1]  1  2  3  4  5  6  7  8  9 10

Vector

  • We can access the value of any element in a vector using []. We can use their position or their value:
x <- c(1, 3, 5)

#Get the value of the second element
x[2]
[1] 3

Vector

  • We can access the value of any element in a vector using []. We can use their position or their value:
x <- c(1, 3, 5)

#Get the value of the first and third element
x[c(1, 3)]
[1] 1 5

Vector

  • We can access the value of any element in a vector using []. We can use their position or their value:
x <- c(1, 3, 5)

#Get all but the first element
x[-1]
[1] 3 5

Factor

A factor is a special kind of character vector that contains ordered categories with an underlying order. This object stores underlying numeric values (\(1\), \(2\), \(\cdots\), \(n\)), but each of these \(n\) values has an associated character label which are called the levels of the factor.

  • We can create factors using the factor() function:
x <- factor(c("underweight", "underweight", "normal", "overweight", "normal"))
x
[1] underweight underweight normal      overweight  normal     
Levels: normal overweight underweight
  • We can see the possible values of a factor using levels():
levels(x)
[1] "normal"      "overweight"  "underweight"

Factor

  • We can use the level argument to specify the levels in the order we want them to appear and label to set the associated label:
#Set levels in the right order
x <- factor(c("underweight", "underweight", "normal", "overweight", "normal"),     
levels = c("underweight", "normal", "overweight"))
x
[1] underweight underweight normal      overweight  normal     
Levels: underweight normal overweight
#Change labels too
x <- factor(c("underweight", "underweight", "normal", "overweight", "normal"),     
levels = c("underweight", "normal", "overweight"), labels = c("Underweight", "Normal", "Overweight"))
x
[1] Underweight Underweight Normal      Overweight  Normal     
Levels: Underweight Normal Overweight

List

An ordered collection of different types of objects

  • We can use the list() function to create a list object
x <- list(c(1, 3, 5), "Character", factor(c("underweight", "underweight", "normal", "overweight", "normal")))
x
[[1]]
[1] 1 3 5

[[2]]
[1] "Character"

[[3]]
[1] underweight underweight normal      overweight  normal     
Levels: normal overweight underweight

List

  • To access an object inside a list we will use [[]]
#Get the first object
x[[1]]
[1] 1 3 5

Dataframe

A special type of list containing vectors of the same length. Vectors containing different types of information (numeric, character, factor, …) are stored in columns forming a tabular data structure.

x <- data.frame(outcome = c(1,0,1,1),
                exposure = c("yes", "yes", "no", "no"),
                age = c(24, 55, 39, 18))
x
  outcome exposure age
1       1      yes  24
2       0      yes  55
3       1       no  39
4       1       no  18
class(x)
[1] "data.frame"

Dataframe

  • To access a column (variable) in a dataset we will use $
x$age
[1] 24 55 39 18
  • To view a dataframe we can use View()
View(x)

Useful tips

Tip

It is useful to use str() to see the class and structure of any object.

x <- data.frame(outcome = c(1,0,1,1),
                exposure = c("yes", "yes", "no", "no"),
                age = c(24, 55, 39, 18))
str(x)
'data.frame':   4 obs. of  3 variables:
 $ outcome : num  1 0 1 1
 $ exposure: chr  "yes" "yes" "no" "no"
 $ age     : num  24 55 39 18

Useful tips

Tip

To quickly see the frequency of the categories of a character vector, use table():

table(x$exposure)

 no yes 
  2   2

Useful tips

Tip

Any object can have missing values that are stored as NA. We can check if an object is missing with the is.na() function:

#Missing value in a single object
x <- NA
is.na(x)
[1] TRUE

Useful tips

Tip

Any object can have missing values that are stored as NA. We can check if an object is missing with the is.na() function:

#Missing value in a numeric vector
x <- c(1, NA, 5)
is.na(x)
[1] FALSE  TRUE FALSE

Useful tips

Tip

Any object can have missing values that are stored as NA. We can check if an object is missing with the is.na() function:

#Missing value in a dataframe
x <- data.frame(outcome = c(1,NA,1,1),
                exposure = c("yes", "yes", NA, "no"),
                age = c(NA, 55, 39, 18))
is.na(x$outcome)
[1] FALSE  TRUE FALSE FALSE
is.na(x$exposure)
[1] FALSE FALSE  TRUE FALSE
is.na(x$age)
[1]  TRUE FALSE FALSE FALSE

Useful tips

Tip

Any object can have missing values that are stored as NA. We can check if an object is missing with the is.na() function:

#Missing value in a dataframe
x <- data.frame(outcome = c(1,NA,1,1),
                exposure = c("yes", "yes", NA, "no"),
                age = c(NA, 55, 39, 18))
  • It is useful to use the table() function to see how many missings an object has:
table(is.na(x$outcome))

FALSE  TRUE 
    3     1

Useful tips

Tip

Names of objects can only contains letters, numbers, _ , or . and must begin with a letter. It is recommended to use short identificative names replacing the spaces with _ .

  • For example, for an object containing the number of daily cigarettes we could use daily_cigar.

Warning

R is case sensitive, so daily_cigar is different to Daily_Cigar.

Numerical and logical operations

Basic numerical operations

  • In R, basic arithmetic operations can be applied to single numeric objects, numeric vectors or dataframes with numeric columns.

  • We can use the arithmetic operators +, -, * or / to sum, substract, multiply or divide.

Note

When you perform an operation on a vector, the operation is automatically applied to each individual element of the vector.

Basic numerical operations

  • Different examples:
#Sum a number to a numeric single object
x <- 1
x + 3
[1] 4

Basic numerical operations

  • Different examples:
#Sum a number to a numeric vector
x <- c(0, 2, 4)
x + 1
[1] 1 3 5

Basic numerical operations

  • Different examples:
#Divide by a number a numeric vector
x <- c(0, 2, 4)
x/2
[1] 0 1 2

Basic numerical operations

  • Different examples:
#Sum up two different vectors
x <- c(0, 2, 4)
y <- c(1, 2, 3)
x + y
[1] 1 4 7

Basic numerical operations

  • Different examples:
#Substract a number to a numeric column of a dataframe
x <- data.frame(outcome = c(1,0,1,1),
                exposure = c("yes", "yes", "no", "no"),
                age = c(24, 55, 39, 18))
x$age - 18
[1]  6 37 21  0

Basic logical operators

  • These are the basic logic operators:
< Less than %in% Included in
> Greater than is.na() Check for missing values
== Equal to !is.na() Check for non-missing values
<= Less than or equal to & AND
>= Greater than or equal to | OR
!= Not equal to ! Negation

Note

When applied to a vector it will be applied elementwise, as before.

Basic logical operators

  • The logical values in R are TRUE or FALSE. When checking the value of an object one of these two values will come up:
#Check if the value is non-missing and lower than 30
x <- 10
!is.na(x) & x < 30
[1] TRUE

Basic logical operators

  • The logical values in R are TRUE or FALSE. When checking the value of an object one of these two values will come up:
#Check if the values in a vector are lower than 20 or equal to 40
x <- c(10, 15, 20, 30, 40)
x < 20 | x == 40
[1]  TRUE  TRUE FALSE FALSE  TRUE

Basic logical operators

  • The logical values in R are TRUE or FALSE. When checking the value of an object one of these two values will come up:
#Check if the value is lower than 20 and also greater than 10 or lower than 100
x <- c(10, 15, 20, 30, 40)
x < 20 & x > 10 | x < 100
[1] TRUE TRUE TRUE TRUE TRUE

Basic logical operators

  • The logical values in R are TRUE or FALSE. When checking the value of an object one of these two values will come up:
#Check if the value is lower than 20 and also greater than 10 or lower than 100
x <- c(10, 15, 20, 30, 40)
x < 20 & x > 10 | x < 100
[1] TRUE TRUE TRUE TRUE TRUE

Warning

Sometimes we need to use parentheses to apply the correct logical expression.

Basic logical operators

  • The logical values in R are TRUE or FALSE. When checking the value of an object one of these two values will come up:
#Check if the value is lower than 20 and also greater than 10 or lower than 100
x <- c(10, 15, 20, 30, 40)
x < 20 & (x > 10 | x < 100)
[1]  TRUE  TRUE FALSE FALSE FALSE

Warning

Sometimes we need to use parentheses to apply the correct logical expression.

Basic logical operators

  • The logical values in R are TRUE or FALSE. When checking the value of an object one of these two values will come up:
#Check which element is different from bananas
x <- c("bananas", "apples", "oranges")
x != "bananas"
[1] FALSE  TRUE  TRUE
x %in% c("apples", "oranges")
[1] FALSE  TRUE  TRUE

Basic logical operators

Tip

To access a vector element, we can use logical operators:

x
[1] "bananas" "apples"  "oranges"
x[x != "bananas"]
[1] "apples"  "oranges"

R functions and packages

R functions

Functions are self contained modules of code that accomplish a specific task.

  • They usually take in some sort of objects (value, vector, dataframe, etc.) called arguments and return a result:

Basic R functions

  • R Base Package has a wide range of useful built-in mathematical and statistical functions:
sqrt() Square root min() Minimum value
abs() Absolute value max() Maximum value
round() Round value mean() Mean value
exp() Exponential value median() Median value
log(), log10() Logarithm of a value sd() Standard deviation

R functions

  • To see how a function works we can use ? :
?median

R packages

A package is an extension to base R that can be downloaded to provide additional functions.

  • It bundles together code, data, documentation and tests.

R packages

  • As of February 2025, there are 22,120 packages available on CRAN.

  • To install and load any package from CRAN:

#Install the package called 'dplyr'
install.packages("dplyr")
#Load it
library(dplyr)

The tidyverse

The tidyverse

A set of R packages ideal for data management. They will make your life a lot easier.

The tidyverse

  • The philosophy of tidyverse is to concatenate basic functions applied to a dataframe to accomplish complex manipulations integrated into a tidy workflow.

  • The tidyverse workflow is based on the usage of the pipe operator, which can be the native pipe (|>) or the magrittr pipe (%>%)

Pipe operator

  • The pipe operator allows to concatenate multiple functions applied to the same object:
#Round π to 6 decimals
round(pi, 6)
[1] 3.141593

Pipe operator

  • The pipe operator allows to concatenate multiple functions applied to the same object:
#Equivalent using pipes
pi |> round(6)
[1] 3.141593

Pipe operator

  • The pipe operator allows to concatenate multiple functions applied to the same object:
#Exponential of the square root of π and then round to 6 decimals
round(exp(sqrt(pi)), 6)
[1] 5.885277

Pipe operator

  • The pipe operator allows to concatenate multiple functions applied to the same object:
#Equivalent using pipes
pi |> 
  sqrt() |> 
  exp() |> 
  round(6)
[1] 5.885277

Tibbles

  • Tibbles are the tidyverse equivalent of a dataframe.

  • They provide better readability and usability within the tidyverse workflow.

Tibbles

  • Tibbles are the tidyverse equivalent of a dataframe.

  • They provide better readability and usability within the tidyverse workflow.

#A dataframe
iris
    Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
1            5.1         3.5          1.4         0.2     setosa
2            4.9         3.0          1.4         0.2     setosa
3            4.7         3.2          1.3         0.2     setosa
4            4.6         3.1          1.5         0.2     setosa
5            5.0         3.6          1.4         0.2     setosa
6            5.4         3.9          1.7         0.4     setosa
7            4.6         3.4          1.4         0.3     setosa
8            5.0         3.4          1.5         0.2     setosa
9            4.4         2.9          1.4         0.2     setosa
10           4.9         3.1          1.5         0.1     setosa
11           5.4         3.7          1.5         0.2     setosa
12           4.8         3.4          1.6         0.2     setosa
13           4.8         3.0          1.4         0.1     setosa
14           4.3         3.0          1.1         0.1     setosa
15           5.8         4.0          1.2         0.2     setosa
16           5.7         4.4          1.5         0.4     setosa
17           5.4         3.9          1.3         0.4     setosa
18           5.1         3.5          1.4         0.3     setosa
19           5.7         3.8          1.7         0.3     setosa
20           5.1         3.8          1.5         0.3     setosa
21           5.4         3.4          1.7         0.2     setosa
22           5.1         3.7          1.5         0.4     setosa
23           4.6         3.6          1.0         0.2     setosa
24           5.1         3.3          1.7         0.5     setosa
25           4.8         3.4          1.9         0.2     setosa
26           5.0         3.0          1.6         0.2     setosa
27           5.0         3.4          1.6         0.4     setosa
28           5.2         3.5          1.5         0.2     setosa
29           5.2         3.4          1.4         0.2     setosa
30           4.7         3.2          1.6         0.2     setosa
31           4.8         3.1          1.6         0.2     setosa
32           5.4         3.4          1.5         0.4     setosa
33           5.2         4.1          1.5         0.1     setosa
34           5.5         4.2          1.4         0.2     setosa
35           4.9         3.1          1.5         0.2     setosa
36           5.0         3.2          1.2         0.2     setosa
37           5.5         3.5          1.3         0.2     setosa
38           4.9         3.6          1.4         0.1     setosa
39           4.4         3.0          1.3         0.2     setosa
40           5.1         3.4          1.5         0.2     setosa
41           5.0         3.5          1.3         0.3     setosa
42           4.5         2.3          1.3         0.3     setosa
43           4.4         3.2          1.3         0.2     setosa
44           5.0         3.5          1.6         0.6     setosa
45           5.1         3.8          1.9         0.4     setosa
46           4.8         3.0          1.4         0.3     setosa
47           5.1         3.8          1.6         0.2     setosa
48           4.6         3.2          1.4         0.2     setosa
49           5.3         3.7          1.5         0.2     setosa
50           5.0         3.3          1.4         0.2     setosa
51           7.0         3.2          4.7         1.4 versicolor
52           6.4         3.2          4.5         1.5 versicolor
53           6.9         3.1          4.9         1.5 versicolor
54           5.5         2.3          4.0         1.3 versicolor
55           6.5         2.8          4.6         1.5 versicolor
56           5.7         2.8          4.5         1.3 versicolor
57           6.3         3.3          4.7         1.6 versicolor
58           4.9         2.4          3.3         1.0 versicolor
59           6.6         2.9          4.6         1.3 versicolor
60           5.2         2.7          3.9         1.4 versicolor
61           5.0         2.0          3.5         1.0 versicolor
62           5.9         3.0          4.2         1.5 versicolor
63           6.0         2.2          4.0         1.0 versicolor
64           6.1         2.9          4.7         1.4 versicolor
65           5.6         2.9          3.6         1.3 versicolor
66           6.7         3.1          4.4         1.4 versicolor
67           5.6         3.0          4.5         1.5 versicolor
68           5.8         2.7          4.1         1.0 versicolor
69           6.2         2.2          4.5         1.5 versicolor
70           5.6         2.5          3.9         1.1 versicolor
71           5.9         3.2          4.8         1.8 versicolor
72           6.1         2.8          4.0         1.3 versicolor
73           6.3         2.5          4.9         1.5 versicolor
74           6.1         2.8          4.7         1.2 versicolor
75           6.4         2.9          4.3         1.3 versicolor
76           6.6         3.0          4.4         1.4 versicolor
77           6.8         2.8          4.8         1.4 versicolor
78           6.7         3.0          5.0         1.7 versicolor
79           6.0         2.9          4.5         1.5 versicolor
80           5.7         2.6          3.5         1.0 versicolor
81           5.5         2.4          3.8         1.1 versicolor
82           5.5         2.4          3.7         1.0 versicolor
83           5.8         2.7          3.9         1.2 versicolor
84           6.0         2.7          5.1         1.6 versicolor
85           5.4         3.0          4.5         1.5 versicolor
86           6.0         3.4          4.5         1.6 versicolor
87           6.7         3.1          4.7         1.5 versicolor
88           6.3         2.3          4.4         1.3 versicolor
89           5.6         3.0          4.1         1.3 versicolor
90           5.5         2.5          4.0         1.3 versicolor
91           5.5         2.6          4.4         1.2 versicolor
92           6.1         3.0          4.6         1.4 versicolor
93           5.8         2.6          4.0         1.2 versicolor
94           5.0         2.3          3.3         1.0 versicolor
95           5.6         2.7          4.2         1.3 versicolor
96           5.7         3.0          4.2         1.2 versicolor
97           5.7         2.9          4.2         1.3 versicolor
98           6.2         2.9          4.3         1.3 versicolor
99           5.1         2.5          3.0         1.1 versicolor
100          5.7         2.8          4.1         1.3 versicolor
101          6.3         3.3          6.0         2.5  virginica
102          5.8         2.7          5.1         1.9  virginica
103          7.1         3.0          5.9         2.1  virginica
104          6.3         2.9          5.6         1.8  virginica
105          6.5         3.0          5.8         2.2  virginica
106          7.6         3.0          6.6         2.1  virginica
107          4.9         2.5          4.5         1.7  virginica
108          7.3         2.9          6.3         1.8  virginica
109          6.7         2.5          5.8         1.8  virginica
110          7.2         3.6          6.1         2.5  virginica
111          6.5         3.2          5.1         2.0  virginica
112          6.4         2.7          5.3         1.9  virginica
113          6.8         3.0          5.5         2.1  virginica
114          5.7         2.5          5.0         2.0  virginica
115          5.8         2.8          5.1         2.4  virginica
116          6.4         3.2          5.3         2.3  virginica
117          6.5         3.0          5.5         1.8  virginica
118          7.7         3.8          6.7         2.2  virginica
119          7.7         2.6          6.9         2.3  virginica
120          6.0         2.2          5.0         1.5  virginica
121          6.9         3.2          5.7         2.3  virginica
122          5.6         2.8          4.9         2.0  virginica
123          7.7         2.8          6.7         2.0  virginica
124          6.3         2.7          4.9         1.8  virginica
125          6.7         3.3          5.7         2.1  virginica
126          7.2         3.2          6.0         1.8  virginica
127          6.2         2.8          4.8         1.8  virginica
128          6.1         3.0          4.9         1.8  virginica
129          6.4         2.8          5.6         2.1  virginica
130          7.2         3.0          5.8         1.6  virginica
131          7.4         2.8          6.1         1.9  virginica
132          7.9         3.8          6.4         2.0  virginica
133          6.4         2.8          5.6         2.2  virginica
134          6.3         2.8          5.1         1.5  virginica
135          6.1         2.6          5.6         1.4  virginica
136          7.7         3.0          6.1         2.3  virginica
137          6.3         3.4          5.6         2.4  virginica
138          6.4         3.1          5.5         1.8  virginica
139          6.0         3.0          4.8         1.8  virginica
140          6.9         3.1          5.4         2.1  virginica
141          6.7         3.1          5.6         2.4  virginica
142          6.9         3.1          5.1         2.3  virginica
143          5.8         2.7          5.1         1.9  virginica
144          6.8         3.2          5.9         2.3  virginica
145          6.7         3.3          5.7         2.5  virginica
146          6.7         3.0          5.2         2.3  virginica
147          6.3         2.5          5.0         1.9  virginica
148          6.5         3.0          5.2         2.0  virginica
149          6.2         3.4          5.4         2.3  virginica
150          5.9         3.0          5.1         1.8  virginica

Tibbles

  • Tibbles are the tidyverse equivalent of a dataframe.

  • They provide better readability and usability within the tidyverse workflow.

#A tibble
iris_tbl
# A tibble: 150 × 5
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
 1          5.1         3.5          1.4         0.2 setosa 
 2          4.9         3            1.4         0.2 setosa 
 3          4.7         3.2          1.3         0.2 setosa 
 4          4.6         3.1          1.5         0.2 setosa 
 5          5           3.6          1.4         0.2 setosa 
 6          5.4         3.9          1.7         0.4 setosa 
 7          4.6         3.4          1.4         0.3 setosa 
 8          5           3.4          1.5         0.2 setosa 
 9          4.4         2.9          1.4         0.2 setosa 
10          4.9         3.1          1.5         0.1 setosa 
# ℹ 140 more rows

The tidyverse

  • This is an example of what a tidyverse workflow looks like compared to base R:
filter_iris <- subset(iris, Species == "setosa")

sel_filter_iris <- filter_iris[, c("Sepal.Length", "Sepal.Width")]

sel_filter_iris$Sepal.Size <- ifelse(sel_filter_iris$Sepal.Length > mean(sel_filter_iris$Sepal.Length) & sel_filter_iris$Sepal.Width > mean(sel_filter_iris$Sepal.Width), 2, 1)

sel_filter_iris$Sepal.Size <- factor(sel_filter_iris$Sepal.Size, levels = 1:2, labels = c("Small", "Big"))

small_iris <- sel_filter_iris[sel_filter_iris$Sepal.Size == "Small",]

small_sepal_area <- mean(small_iris[,"Sepal.Length"] * small_iris[,"Sepal.Width"])

big_iris <- sel_filter_iris[sel_filter_iris$Sepal.Size == "Big",]

big_sepal_area <- mean(big_iris[,"Sepal.Length"] * big_iris[,"Sepal.Width"])

data.frame(
  "Sepal.Size" = c("Small", "Big"),
  "Sepal.Area" = c(small_sepal_area, big_sepal_area)
)
  Sepal.Size Sepal.Area
1      Small   15.65636
2        Big   20.36647

The tidyverse

  • This is an example of what a tidyverse workflow looks like compared to base R:
library(dplyr)

iris |> 
  filter(Species == "setosa") |> 
  select(Sepal.Length, Sepal.Width) |> 
  mutate(
    Sepal.Size = case_when(
      Sepal.Length > mean(Sepal.Length) & Sepal.Width > mean(Sepal.Width) ~ 2,
      .default = 1
    ),
    Sepal.Size = factor(Sepal.Size, levels = 1:2, labels = c("Small", "Big")) 
  ) |> 
  group_by(Sepal.Size) |> 
  summarise(
    Sepal.Area = mean(Sepal.Length*Sepal.Width)
  )
# A tibble: 2 × 2
  Sepal.Size Sepal.Area
  <fct>           <dbl>
1 Small            15.7
2 Big              20.4

The tidyverse

  • In comparison with base R, the tidyverse provides:

    • Consistent & Readable Syntax: uses a uniform and intuitive grammar.

    • Efficient Data Management: specific functions that simplify a lot data wrangling and reduce the risk of mistakes.

    • Better Data Visualization: {ggplot2} is the most powerful and popular tool for creating graphics in R.

    • Easier to Learn for Beginners: more human-readable than base R’s indexing and function chaining.

Reading data

Reading data

  • Before performing any analysis, it’s essential to import your data into R. R supports various data formats, including:

→ Text files

→ CSV files

→ Excel

→ SPSS

→ STATA

  • Common path issues:
    • R may not locate your file if the path is incorrect. Double-check the path for typos.
    • On Windows, ensure you use double backslashes (\\) or forward slashes (/) in the path, for example:
      • C:\\Users\\YourName\\Documents\\file.csv
      • C:/Users/YourName/Documents/file.csv
      • r"(C:\Users\YourName\Documents\file.csv)"

Reading data - Text Files

  • Text files (.txt) is a common format for storing tabular data.
my_data <- read.table("file.txt")


Key arguments:

→ skip: Number of lines to skip before reading the data.

→ header: Indicates if the first row contains column names (default is TRUE).

→ sep: Specifies the field separator character (default is any whitespace).

Reading data - CSV Files

  • CSV (Comma-Separated Values) is also a common format for storing tabular data.
my_data <- read.csv("file.csv")


Key arguments:

→ skip: Number of lines to skip before reading the data.

→ header: Indicates if the first row contains column names (default is TRUE).

→ stringsAsFactors: Determines whether character vectors should be converted to factors (default is FALSE).

Reading data - Excel Files

  • Excel files (.xlsx) can be imported using the readxl package.
library(readxl)
my_data <- read_excel("file.xlsx")


Key arguments:

→ startRow: Specifies the first row to begin reading data.

→ detectDates: Automatically detects date formats and converts them accordingly.

→ skipEmptyCols: Skips empty columns when reading the file.

Reading data - SPSS Files

  • SPSS (.sav) files can be imported using the haven package.
library(haven)
my_data <- read_sav("file.sav")


Key arguments:

→ skip: Number of lines to skip before reading data.

→ col_select: Select specific columns to import.

Reading data - STATA Files

  • STATA (.dta) files can also be imported using the haven package.
library(haven)
my_data <- read_dta("file.dta")


Key arguments:

→ skip: Number of lines to skip before reading data.

→ col_select: Select specific columns to import.

Reading data - Example


library(readxl)
my_data <- read_excel("C:\\Users\\X\\Downloads\\dataset_example.xlsx")

View(my_data)
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1           5.1         3.5          1.4         0.2  setosa
2           4.9         3.0          1.4         0.2  setosa
3           4.7         3.2          1.3         0.2  setosa
4           4.6         3.1          1.5         0.2  setosa
5           5.0         3.6          1.4         0.2  setosa
6           5.4         3.9          1.7         0.4  setosa
7           4.6         3.4          1.4         0.3  setosa
8           5.0         3.4          1.5         0.2  setosa
9           4.4         2.9          1.4         0.2  setosa
10          4.9         3.1          1.5         0.1  setosa

…

Dataframe - Creating one

Recapping:

A dataframe is a special type of list containing vectors of the same length. These vectors containing different types of information (numeric, character, factor, …) are stored in columns forming a tabular data structure.

df <- data.frame(
  "id" = 1:4,
  "fruit" = as.character(c("apple", "banana", "banana", "orange")), 
  "size" = c(1.2, 4.5, 5.6, 3.4), 
  "colour" = factor(c("red", "yellow", "yellow", "orange"))
)

Dataframe - Basic elements

  • Number of rows:
nrow(df)
[1] 4


  • Number of columns:
ncol(df)
[1] 4


  • Names of columns:
names(df)
[1] "id"     "fruit"  "size"   "colour"

Dataframe - Details

  • Class of each column:
str(df)
'data.frame':   4 obs. of  4 variables:
 $ id    : int  1 2 3 4
 $ fruit : chr  "apple" "banana" "banana" "orange"
 $ size  : num  1.2 4.5 5.6 3.4
 $ colour: Factor w/ 3 levels "orange","red",..: 2 3 3 1


  • Preview of the dataframe:
head(df, 3)
  id  fruit size colour
1  1  apple  1.2    red
2  2 banana  4.5 yellow
3  3 banana  5.6 yellow

Dataframe - Details

  • Class of each column:
str(df)
'data.frame':   4 obs. of  4 variables:
 $ id    : int  1 2 3 4
 $ fruit : chr  "apple" "banana" "banana" "orange"
 $ size  : num  1.2 4.5 5.6 3.4
 $ colour: Factor w/ 3 levels "orange","red",..: 2 3 3 1


  • Preview of the dataframe:
tail(df, 2)
  id  fruit size colour
3  3 banana  5.6 yellow
4  4 orange  3.4 orange

Dataframe - Column access


id fruit size colour
1 apple 1.2 red
2 banana 4.5 yellow
3 banana 5.6 yellow
4 orange 3.4 orange

Dataframe - Column access


id fruit size colour
1 apple 1.2 red
2 banana 4.5 yellow
3 banana 5.6 yellow
4 orange 3.4 orange

Dataframe - Column access


id fruit size colour
1 apple 1.2 red
2 banana 4.5 yellow
3 banana 5.6 yellow
4 orange 3.4 orange


Accessing one column:

df$id
[1] 1 2 3 4


df[["id"]]
[1] 1 2 3 4


df[, "id"]
[1] 1 2 3 4

Dataframe - Row access


id fruit size colour
1 apple 1.2 red
2 banana 4.5 yellow
3 banana 5.6 yellow
4 orange 3.4 orange

Dataframe - Row access


id fruit size colour
1 apple 1.2 red
2 banana 4.5 yellow
3 banana 5.6 yellow
4 orange 3.4 orange


Accessing one row:

df[1, ]
  id fruit size colour
1  1 apple  1.2    red

Dataframe - Cell access


id fruit size colour
1 apple 1.2 red
2 banana 4.5 yellow
3 banana 5.6 yellow
4 orange 3.4 orange

Dataframe - Cell access


id fruit size colour
1 apple 1.2 red
2 banana 4.5 yellow
3 banana 5.6 yellow
4 orange 3.4 orange


Accessing a specific value:

df$fruit[4]
[1] "orange"


df[4, "fruit"]
[1] "orange"


df[4, 2]
[1] "orange"

Data management

{dplyr} - Installation

  • First, install and load the dplyr package into your R session:
install.packages("dplyr")
library(dplyr)
Warning: package 'dplyr' was built under R version 4.4.3

Attaching package: 'dplyr'
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

{dplyr} - Installation

  • Consult the package help:
?dplyr

{dplyr} - Overview

  • The functions in dplyr can be categorized based on the dataset components they work with:

Columns:

  • select()

  • rename()

  • relocate()

  • pull()

  • mutate()

Rows:

  • filter()

  • arrange()

Groups of rows:

  • summarise()

{dplyr} - Example dataset

  • Before proceeding, let’s load an example dataset from the gtsummary package:
library(gtsummary)
trial
  • Overview:
head(trial)
# A tibble: 6 × 8
  trt      age marker stage grade response death ttdeath
  <chr>  <dbl>  <dbl> <fct> <fct>    <int> <int>   <dbl>
1 Drug A    23  0.16  T1    II           0     0    24  
2 Drug B     9  1.11  T2    I            1     0    24  
3 Drug A    31  0.277 T1    II           0     0    24  
4 Drug A    NA  2.07  T3    III          1     1    17.6
5 Drug A    51  2.77  T4    III          1     1    16.4
6 Drug B    39  0.613 T4    I            0     1    15.6

{dplyr} - Select columns

Function: select()


trt age marker stage grade response death ttdeath
Drug A 23 0.160 T1 II 0 0 24.00
Drug B 9 1.107 T2 I 1 0 24.00
Drug A 31 0.277 T1 II 0 0 24.00
Drug A NA 2.067 T3 III 1 1 17.64
Drug A 51 2.767 T4 III 1 1 16.43



→ Select columns by position:

trial |> 
  select(1:4)
     trt age marker stage
1 Drug A  23  0.160    T1
2 Drug B   9  1.107    T2
3 Drug A  31  0.277    T1
4 Drug A  NA  2.067    T3
5 Drug A  51  2.767    T4

{dplyr} - Select columns

Function: select()


trt age marker stage grade response death ttdeath
Drug A 23 0.160 T1 II 0 0 24.00
Drug B 9 1.107 T2 I 1 0 24.00
Drug A 31 0.277 T1 II 0 0 24.00
Drug A NA 2.067 T3 III 1 1 17.64
Drug A 51 2.767 T4 III 1 1 16.43



→ Creating the example dataset:

trial2 <- trial |> 
  select(1:4)
     trt age marker stage
1 Drug A  23  0.160    T1
2 Drug B   9  1.107    T2
3 Drug A  31  0.277    T1
4 Drug A  NA  2.067    T3
5 Drug A  51  2.767    T4

Complete this step, since the upcoming examples will rely on this dataframe.

{dplyr} - Select columns

Function: select()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4



→ Select specific columns:

trial2 |> 
  select(trt, age)
     trt age
1 Drug A  23
2 Drug B   9
3 Drug A  31
4 Drug A  NA
5 Drug A  51

{dplyr} - Select columns

Function: select()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4



→ Remove specific columns:

trial2 |> 
  select(-trt)
  age marker stage
1  23  0.160    T1
2   9  1.107    T2
3  31  0.277    T1
4  NA  2.067    T3
5  51  2.767    T4

{dplyr} - Select columns

Function: select()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4



→ Select a range of columns:

trial2 |> 
  select(trt:marker)
     trt age marker
1 Drug A  23  0.160
2 Drug B   9  1.107
3 Drug A  31  0.277
4 Drug A  NA  2.067
5 Drug A  51  2.767

{dplyr} - Select columns

Function: select()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4



→ Remove a range of columns:

trial2 |> 
  select(-(trt:marker))
  stage
1    T1
2    T2
3    T1
4    T3
5    T4

{dplyr} - Select columns

Function: select()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4



→ Select columns starting with:

trial2 |> 
  select(starts_with("m"))
  marker
1  0.160
2  1.107
3  0.277
4  2.067
5  2.767

{dplyr} - Select columns

Function: select()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4



→ Select columns ending with:

trial2 |> 
  select(ends_with("ge"))
  age stage
1  23    T1
2   9    T2
3  31    T1
4  NA    T3
5  51    T4

{dplyr} - Select columns

Function: select()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4



→ Mixed example:

trial2 |> 
  select(trt, starts_with("a"), ends_with("ge"))
     trt age stage
1 Drug A  23    T1
2 Drug B   9    T2
3 Drug A  31    T1
4 Drug A  NA    T3
5 Drug A  51    T4

{dplyr} - Change column names

Function: rename()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4



→ Renaming one variable:

trial2 |> 
  rename("Treatment" = "trt")
  Treatment age marker stage
1    Drug A  23  0.160    T1
2    Drug B   9  1.107    T2
3    Drug A  31  0.277    T1
4    Drug A  NA  2.067    T3
5    Drug A  51  2.767    T4

{dplyr} - Change column names

Function: rename()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4



→ Renaming several variables:

trial2 |> 
  rename("Treatment" = "trt", "Age" = "age", "Marker (ng/mL)" = "marker")
  Treatment Age Marker (ng/mL) stage
1    Drug A  23          0.160    T1
2    Drug B   9          1.107    T2
3    Drug A  31          0.277    T1
4    Drug A  NA          2.067    T3
5    Drug A  51          2.767    T4

For clear and efficient data processing, avoid spaces, special characters, and uppercase letters.

{dplyr} - Change columns positions

Function: relocate()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4



→ Relocating a variable:

trial2 |> 
  relocate(stage)
  stage    trt age marker
1    T1 Drug A  23  0.160
2    T2 Drug B   9  1.107
3    T1 Drug A  31  0.277
4    T3 Drug A  NA  2.067
5    T4 Drug A  51  2.767

{dplyr} - Change columns positions

Function: relocate()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4



→ Relocating a variable:

trial2 |> 
  relocate(stage, .before = age)
     trt stage age marker
1 Drug A    T1  23  0.160
2 Drug B    T2   9  1.107
3 Drug A    T1  31  0.277
4 Drug A    T3  NA  2.067
5 Drug A    T4  51  2.767

{dplyr} - Change columns positions

Function: relocate()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4



→ Relocating a variable:

trial2 |> 
  relocate(stage, .after = age)
     trt age stage marker
1 Drug A  23    T1  0.160
2 Drug B   9    T2  1.107
3 Drug A  31    T1  0.277
4 Drug A  NA    T3  2.067
5 Drug A  51    T4  2.767

{dplyr} - Change columns positions

Function: relocate()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4



→ Relocating several variables:

trial2 |> 
  relocate(marker, stage, .after = trt)
     trt marker stage age
1 Drug A  0.160    T1  23
2 Drug B  1.107    T2   9
3 Drug A  0.277    T1  31
4 Drug A  2.067    T3  NA
5 Drug A  2.767    T4  51

{dplyr} - Access specific columns

Function: pull()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4



→ Accessing a column:

trial2 |> 
  pull("trt")
[1] "Drug A" "Drug B" "Drug A" "Drug A" "Drug A"


trial2 |> 
  pull(1)
[1] "Drug A" "Drug B" "Drug A" "Drug A" "Drug A"

{dplyr} - Create/modify columns

Function: mutate()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4



→ Creating a new column:

trial2 |> 
  mutate(visit = 1)
     trt age marker stage visit
1 Drug A  23  0.160    T1     1
2 Drug B   9  1.107    T2     1
3 Drug A  31  0.277    T1     1
4 Drug A  NA  2.067    T3     1
5 Drug A  51  2.767    T4     1

{dplyr} - Create/modify columns

Function: mutate()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4



→ Creating a new column using others:

trial2 |> 
  mutate(marker2 = marker * 100)
     trt age marker stage marker2
1 Drug A  23  0.160    T1    16.0
2 Drug B   9  1.107    T2   110.7
3 Drug A  31  0.277    T1    27.7
4 Drug A  NA  2.067    T3   206.7
5 Drug A  51  2.767    T4   276.7

{dplyr} - Create/modify columns

Function: mutate()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4



→ Creating multiple new columns:

trial2 |> 
  mutate(visit = 1, marker2 = marker * 100)
     trt age marker stage visit marker2
1 Drug A  23  0.160    T1     1    16.0
2 Drug B   9  1.107    T2     1   110.7
3 Drug A  31  0.277    T1     1    27.7
4 Drug A  NA  2.067    T3     1   206.7
5 Drug A  51  2.767    T4     1   276.7

{dplyr} - Create/modify columns

Function: mutate()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4



→ Transforming an existing column:

trial2 |> 
  mutate(trt = tolower(trt))
     trt age marker stage
1 drug a  23  0.160    T1
2 drug b   9  1.107    T2
3 drug a  31  0.277    T1
4 drug a  NA  2.067    T3
5 drug a  51  2.767    T4

{dplyr} - Create/modify columns (Tip)

Function: mutate()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4



Tip: Create a variable using case_when:

trial2 |> 
  mutate(trt_num = case_when(
    trt == "Drug A" ~ 1, 
    .default = 0
  ))
     trt age marker stage trt_num
1 Drug A  23  0.160    T1       1
2 Drug B   9  1.107    T2       0
3 Drug A  31  0.277    T1       1
4 Drug A  NA  2.067    T3       1
5 Drug A  51  2.767    T4       1

{dplyr} - Filter by specific rows

Function: filter()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4



→ Filtering using one condition:

trial2 |> 
  filter(age > 18)
     trt age marker stage
1 Drug A  23  0.160    T1
2 Drug A  31  0.277    T1
3 Drug A  51  2.767    T4
4 Drug B  39  0.613    T4
5 Drug A  37  0.354    T1

{dplyr} - Filter by specific rows

Function: filter()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4



→ Filtering using a logical condition:

trial2 |> 
  filter(age > 18 & stage == "T1")
     trt age marker stage
1 Drug A  23  0.160    T1
2 Drug A  31  0.277    T1
3 Drug A  37  0.354    T1
4 Drug A  32  1.739    T1
5 Drug A  31  0.144    T1

{dplyr} - Filter by specific rows

Function: filter()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4



→ Filtering using a logical condition:

trial2 |> 
  filter(age > 18 | stage == "T2")
     trt age marker stage
1 Drug A  23  0.160    T1
2 Drug B   9  1.107    T2
3 Drug A  31  0.277    T1
4 Drug A  51  2.767    T4
5 Drug B  39  0.613    T4

{dplyr} - Sort the dataset

Function: arrange()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4



→ Ascending ordering:

trial2 |> 
  arrange(age)
     trt age marker stage
1 Drug A   6  0.229    T4
2 Drug B   9  1.107    T2
3 Drug B  10  0.358    T4
4 Drug A  17  0.039    T4
5 Drug A  19  0.022    T1

{dplyr} - Sort the dataset

Function: arrange()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4



→ Descending ordering:

trial2 |> 
  arrange(-age)
     trt age marker stage
1 Drug B  83  0.475    T1
2 Drug A  78  0.175    T3
3 Drug B  76  1.882    T3
4 Drug A  76  1.550    T1
5 Drug A  75  0.092    T4

{dplyr} - Compute summary statistics

Function: summarise()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4



→ One variable:

trial2 |> 
  summarise(age_min = min(age))
  age_min
1      NA

{dplyr} - Compute summary statistics

Function: summarise()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4



→ One variable (removing missings):

trial2 |> 
  summarise(age_min = min(age, na.rm = T))
  age_min
1       6

{dplyr} - Compute summary statistics

Function: summarise()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4



→ Multiple variables:

trial2 |> 
  summarise(age_min = min(age, na.rm = T), 
            age_max = max(age, na.rm = T))
  age_min age_max
1       6      83

{dplyr} - Group rows

Function: group_by()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4



→ Group rows by treatment:

trial2 |> 
  group_by(trt)

{dplyr} - Group rows

Function: group_by()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4

Arrow Image

trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4

{dplyr} - Group rows

Function: group_by()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4 
trial2 |> 
  group_by(trt) |> 
  summarise(marker_mean = mean(marker, na.rm = T))
     trt marker_mean
1 Drug A   1.0173478
2 Drug B   0.8208367

{dplyr} - Group rows

Function: group_by()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4 
trial2 |> 
  group_by(trt) |> 
  mutate(marker_mean = mean(marker, na.rm = T))
     trt age marker stage marker_mean
1 Drug A  23  0.160    T1   1.0173478
2 Drug B   9  1.107    T2   0.8208367
3 Drug A  31  0.277    T1   1.0173478
4 Drug A  NA  2.067    T3   1.0173478
5 Drug A  51  2.767    T4   1.0173478

{dplyr} - Group rows

Function: group_by()


trt age marker stage
Drug A 23 0.160 T1
Drug B 9 1.107 T2
Drug A 31 0.277 T1
Drug A NA 2.067 T3
Drug A 51 2.767 T4 
trial2 |> 
  group_by(trt) |> 
  mutate(marker_mean = mean(marker, na.rm = T)) |> 
  ungroup()

Warning

Always put ungroup() when you’ve finished with your calculations by group.

Joining data

{dplyr} - Joining datasets


  • For the following examples we will use these built-in datasets:
head(band_members)
  name    band
1 Mick  Stones
2 John Beatles
3 Paul Beatles
head(band_instruments)
   name  plays
1  John guitar
2  Paul   bass
3 Keith guitar

{dplyr} - Left join

Function: left_join()


Members
name band
Mick Stones
John Beatles
Paul Beatles
Instruments
name plays
John guitar
Paul bass
Keith guitar 



band_members |> 
  left_join(band_instruments)

{dplyr} - Left join

Function: left_join()




band_members |> 
  left_join(band_instruments)
Joining with `by = join_by(name)`
Joint
name band plays
Mick Stones NA
John Beatles guitar
Paul Beatles bass

{dplyr} - Left join

Function: left_join()


Members
name band
Mick Stones
John Beatles
Paul Beatles
Instruments
name plays
John guitar
Paul bass
Keith guitar
Joint
name band plays
Mick Stones NA
John Beatles guitar
Paul Beatles bass

{dplyr} - Left join

Function: left_join()


Members
name band
Mick Stones
John Beatles
Paul Beatles
Instruments
name plays
John guitar
Paul bass
Keith guitar
Joint
name band plays
Mick Stones NA
John Beatles guitar
Paul Beatles bass

{dplyr} - Left join

Function: left_join()



→ Specifying the by argument:

band_members |> 
  left_join(band_instruments, by = "name")
Joint
name band plays
Mick Stones NA
John Beatles guitar
Paul Beatles bass

Pivoting data

{tidyr} - Installation


  • Firstly, we install it in the usual way and load the package in the session:
install.packages("tidyr")
library(tidyr)


  • To consult the help on the package:
?tidyr

{tidyr} - Wide to long format dataset

Function: pivot_longer()


→ Based on what we’ve learned, let’s create a pre-treatment marker (marker_pre) and a post-treatment marker (marker_post) columns:


trial2_wide <- trial2 |> 
  mutate("id" = 1:nrow(trial2),
         "marker_post" = marker + 1) |> 
  rename("marker_pre" = "marker") |> 
  relocate(id) |> 
  relocate(stage, .before = marker_pre)
id trt age stage marker_pre marker_post
1 Drug A 23 T1 0.160 1.160
2 Drug B 9 T2 1.107 2.107
3 Drug A 31 T1 0.277 1.277
4 Drug A NA T3 2.067 3.067
5 Drug A 51 T4 2.767 3.767

{tidyr} - Wide to long format dataset

Function: pivot_longer()


→ Transforming the marker_pre and marker_post columns into two new columns. One with the time points (time) and one for the marker values (marker):


trial2_wide |> 
  pivot_longer(
    cols = starts_with("marker_"), 
    names_to = "time", 
    values_to = "marker"
  )
id trt age stage time marker
1 Drug A 23 T1 marker_pre 0.160
1 Drug A 23 T1 marker_post 1.160
2 Drug B 9 T2 marker_pre 1.107
2 Drug B 9 T2 marker_post 2.107
3 Drug A 31 T1 marker_pre 0.277
3 Drug A 31 T1 marker_post 1.277

{tidyr} - Long to wide format dataset

Function: pivot_wider()


→ For this example, let’s again use the trial2 dataset and create a new id (id) column:


trial2_long <- trial2 |> 
  mutate(id = 1:nrow(trial2)) |> 
  relocate(id)
id trt age marker stage
1 Drug A 23 0.160 T1
2 Drug B 9 1.107 T2
3 Drug A 31 0.277 T1
4 Drug A NA 2.067 T3
5 Drug A 51 2.767 T4

{tidyr} - Long to wide format dataset

Function: pivot_wider()


→ Spread the marker values across columns based on the categories of the treatment variable (trt):


trial2_long |> 
  pivot_wider(
    names_from = "trt", 
    values_from = "marker"
  )
id age stage Drug A Drug B
1 23 T1 0.160 NA
2 9 T2 NA 1.107
3 31 T1 0.277 NA
4 NA T3 2.067 NA
5 51 T4 2.767 NA

Why use {dplyr} and {tidyr}?

  • {dplyr} – Efficient Data Management

    → Filter, sort, and transform data (filter(), arrange(), mutate()) ✅

    → Summarize data with powerful grouping (group_by(), summarise()) ✅

    → Join datasets effortlessly (left_join()) ✅


  • {tidyr} – Reshape Data

    → Convert between data formats (pivot_wider(), pivot_longer()) ✅

Mastering these tools makes data analysis in R more intuitive, efficient, and reproducible. 🎯


📅 Thanks & See You Tomorrow! 👋

Applied Biostatistics Course with R